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Field
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to work on the design and implementation of Oscillatory Neural Networks (ONNs) for physics-based computing applications. You as the successful candidate will be an integral part of the prestigious ERC
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to work on the design and implementation of Oscillatory Neural Networks (ONNs) for physics-based computing applications. You as the candidate will be an integral part of the prestigious NWO AiNED AI-on-ONN
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: Developing novel techniques to understand how information is processed within deep neural networks. Developing methods that achieve high accuracy while also being safe, interpretable, responsible, and reliable
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development, especially with neural networks. Experience with standard software development tools (Git, CI/CD, IDEs, issue tracking). Strong interest in academic research and willingness to pursue a PhD
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related field; a strong interest in machine learning for health, environment or public policy; an experience with Deep learning, Time-series or spatio-temporal modelling, Graph neural networks or geospatial
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cells Key methods will include: Gaussian Processes (heteroscedastic & multivariate) Operator-valued and deep kernels Active Bayesian experimental design Physics-informed neural networks Closed-loop
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patient-centred organizations across Europe. Through this collaborative, interdisciplinary network, our researchers will work at the frontier of personalized neuroscience. Where to apply Website https
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) modules into safety-critical embedded systems (autonomous vehicles, drones, industrial and medical devices) raises major safety and security concerns. These modules, often based on deep neural networks
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organizational levels of the brain – from molecular and cellular processes to complex neuronal networks and behavior. In association with the SFB 1436, Neural Resources of Cognition (supported by the German
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the design and analysis of such models. PhD position 1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD